CAVALCANTI, J. H. F.; http://lattes.cnpq.br/6012032994964522; CAVALCANTI, José Homero Feitosa.
Resumen:
The principal objective of this Thesis is to demonstrate that for a class of non-linear
systems with partially known models, it is possible to employ a multi-layer artificial neural
network (MLANN), for an adaptative control strategy, without the need for off-line training of
the neural network.
Initially, some experimental results, as well as simulation results, related with the
position and speed control of a d.c. motor are presented. The experimental results were
obtained both with the conventional controllers like the PID controller and reference model
adaptive controller and the non-conventional controller like a neural network controller. Based
on the simulations studies and comparisons of the performance of the conventional and neural
controllers, the configuration of a neural network controller is proposed.
Using the principal neural controller strategies proposed in the literature, and more
specifically the direct and indirect controllers and the ones based on non-linear functions,
experimental studies were carried out for a d.c. motor drive system with these controllers.
These investigations revealed that if the jacobian of a plant is known, it is possible to
transform the direct neural controller into an adaptive neural controller. Based on the plant
jacobian, the concept of a passive state for the plant under control is introduced. The use of
this concept enables the on-line training o f the multi-layer artificial neural network in real time
with a fair degree of reliability and the necessity for a prolonged off-line training of the
MLANN is thus avoided. This also affords a possibility for on-line fine-tuning of the neural
controller.
Some architectural aspects and other characteristics of the MLANN, as an adaptive
controller, have been verified. A method to determine the optimum number of neurons in the
hidden layer is presented. The adaptation factor for a direct adaptive neural controller is
defined and an experimental procedure to determine its value, is presented.
With a view to experimentally demonstrate the possibility of the generalization of the
direct neural adaptive controller, concepts of fuzzy control are combined with those of the
neural control to implement a position controller for a rigid inverted pendulum arm mounted
on the d.c. motor shaft.